<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to allow vehicles to communicate with other vehicles, humans, pedestrians, roadside units, and other infrastructures. Two potential technologies of V2X communication are dedicated short-range communication (DSRC) and cellular network technologies. Each of these has its benefits and limitations. DSRC has low latency but it limits coverage area and lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell coverage range, but the drawback is its high transmission time intervals. 5G offers enormous benefits to the present wireless communication technology by providing higher data rates and very low latencies for transmissions but is prone to blockages because of its inability to penetrate through the objects. Hence, considering the above issues, single technology will not fully accommodate the V2X requirements which subsequently jeopardize the effectiveness of safety applications. Therefore, for efficient V2X communication, it is required to interwork with DSRC and cellular network technologies. One open research challenge that has gained the attention of the research community over the past few years is the appropriate selection of networks for handover in a heterogeneous IoV environment. Existing solutions have addressed the issues related to handover and network selection but they have failed to address the need for handover while selecting the network. Previous studies have only mentioned that the network is being selected directly for handover or it was connected to the available radio access. Due to this, the occurrence of handover had to take place frequently. Hence, in this research, the integration of DSRC, LTE, and mmWave 5G is incorporated with handover decision, network selection, and routing algorithms. The handover decision is to ensure whether there is a need for vertical handover by using a dynamic Q-learning algorithm. Then, the network selection is based on a fuzzy-convolution neural network that creates fuzzy rules from signal strength, distance, vehicle density, data type, and line of sight. V2V chain routing is proposed to select V2V pairs using a jellyfish optimization algorithm that takes into account the channel, vehicle characteristics, and transmission metrics. This system is developed in an OMNeT++ simulator and the performances are evaluated in terms of mean handover, handover failure, mean throughput, delay, and packet loss.</p></abstract>

Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to allow vehicles to communicate with other vehicles, humans, pedestrians, roadside units, and other infrastructures. Two potential technologies of V2X communication are dedicated short-range communication (DSRC) and cellular network technologies. Each of these has its benefits and limitations. DSRC has low latency but it limits coverage area and lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell coverage range, but the drawback is its high transmission time intervals. 5G offers enormous benefits to the present wireless communication technology by providing higher data rates and very low latencies for transmissions but is prone to blockages because of its inability to penetrate through the objects. Hence, considering the above issues, single technology will not fully accommodate the V2X requirements which subsequently jeopardize the effectiveness of safety applications. Therefore, for efficient V2X communication, it is required to interwork with DSRC and cellular network technologies. One open research challenge that has gained the attention of the research community over the past few years is the appropriate selection of networks for handover in a heterogeneous IoV environment. Existing solutions have addressed the issues related to handover and network selection but they have failed to address the need for handover while selecting the network. Previous studies have only mentioned that the network is being selected directly for handover or it was connected to the available radio access. Due to this, the occurrence of handover had to take place frequently. Hence, in this research, the integration of DSRC, LTE, and mmWave 5G is incorporated with handover decision, network selection, and routing algorithms. The handover decision is to ensure whether there is a need for vertical handover by using a dynamic Q-learning algorithm. Then, the network selection is based on a fuzzy-convolution neural network that creates fuzzy rules from signal strength, distance, vehicle density, data type, and line of sight. V2V chain routing is proposed to select V2V pairs using a jellyfish optimization algorithm that takes into account the channel, vehicle characteristics, and transmission metrics. This system is developed in an OMNeT++ simulator and the performances are evaluated in terms of mean handover, handover failure, mean throughput, delay, and packet loss.

Performance evaluation of vertical handover in Internet of Vehicles

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Vertical Handover (VHO) in Internet of Vehicles (IoV):

Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to...

Method	Average number of HO	Better efficiency	Average HO_FR	Better efficiency
Conventional	5.51	55%	0.133	90%
TOPSIS	2.15	20%	0.041	40%
ANDSF-HO	3.57	30%	0.069	60%
V2I-MoLoHA	3.03	25%	0.029	20%
Proposed	1.30	–	0.01	–

	Input
Rule number	S_r	Distance	V_D	Data type	LoS	Output
R1	H	H	H	H	H	H
R2	H	H	H	H	L	H
R3	H	H	H	L	H	M
R4	H	H	H	L	L	M
R5	H	H	L	H	H	H
R6	H	H	L	H	L	M
R7	H	H	L	L	H	L
R8	H	H	L	L	L	M
R9	H	L	H	H	H	H
R10	H	L	H	H	L	L
R11	H	L	H	L	H	L
R12	H	L	H	L	L	L
R13	H	L	L	H	H	H
R14	H	L	L	H	L	M
R15	H	L	L	L	H	L
R16	H	L	L	L	L	M
R17	L	H	H	H	H	H
R18	L	H	H	H	L	L
R19	L	H	H	L	H	H
R20	L	H	H	L	L	M
R21	L	H	L	H	H	H
R22	L	H	L	H	L	H
R23	L	H	L	L	H	L
R24	L	H	L	L	L	L
R25	L	L	H	H	H	H
R26	L	L	H	H	L	M
R27	L	L	H	L	H	L
R28	L	L	H	L	L	L
R29	L	L	L	H	H	M
R30	L	L	L	H	L	M
R31	L	L	L	L	H	L
R32	L	L	L	L	L	L

Parameter	Range/Value
Simulation area	2,500 m × 2,500 m
Number of vehicles	100
Number of 5G mmWave BSs	2
Number of 4G LTE BSs	2
Vehicle mobility type	Linear mobility
Vehicle speed	10-40 m/s
Transmission range
DSRC	300 m (Max)
mmWave	~500 m
LTE	100 km (Max)
Transmission rate	3-5 packets per second
Packet size	512 bytes
Simulation time	1,000 sec

Method	Mean $T$ (kbps)	Better efficiency	Average delay (ms)	Better efficiency
Conventional	13.7	46%	39	21%
TOPSIS	35.96	23%	30	12%
ANDSF-HO	25	34%	37	19%
V2I-MoLoHA	31.89	27%	34	16%
Proposed	58.89	–	18	–

Method	Packet loss (%)	Better efficiency
Conventional	48	21%
TOPSIS	32.4	12%
ANDSF-HO	24	19%
V2I-MoLoHA	18.8	16%
Proposed	12	–

Performance evaluation of vertical handover in Internet of Vehicles

Published Online: Jun 28, 2021

Page range: 1 - 16

Received: Jan 16, 2021

DOI: https://doi.org/10.21307/ijssis-2021-012

Keywords
4G LTE, DSRC, Internet of Vehicles, mmwave 5G, Network selection, Vertical handover

© 2021 Shaik Mazhar Hussain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Comparison of HO efficiency.

Fuzzy rules.

Simulation specifications.

Comparison of throughput and delay.

Comparison of packet loss.

Performance evaluation of vertical handover in Internet of Vehicles

Published Online: Jun 28, 2021

Page range: 1 - 16

Received: Jan 16, 2021

DOI: https://doi.org/10.21307/ijssis-2021-012

Keywords4G LTE, DSRC, Internet of Vehicles, mmwave 5G, Network selection, Vertical handover

© 2021 Shaik Mazhar Hussain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Comparison of HO efficiency.

Fuzzy rules.

Simulation specifications.

Comparison of throughput and delay.

Comparison of packet loss.

Keywords
4G LTE, DSRC, Internet of Vehicles, mmwave 5G, Network selection, Vertical handover